Designing a web-based health information system

8/10/2019 Designing a web-based health information system

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Designing a Web-based Health Information

System Using Photoplethysmogram (PPG) Signal

AbstractThis paper researchs about the non-invasive meth-ods of measuring health parameters based on the optical signalPhotoplethysmography (PPG). Then, this paper proposes themethods of processing PPG signals using Wavelet transform.From the theoretical research, this paper proposes to designand build a web-based system to monitor the health parametersusing PPG signals. The PPG signals will be processed onSAM4E-EK board to obtain the desired results, then they arestored in a database and displayed on the website for remotemonitoring.

Index Termsphotoplethysmography, PPG, NIBP, SAM4E-EK, health information, non-invasive, blood glucose, bloodpressure, heart rate.

I. INTRODUCTION

With the development of science and technology, there are

plenty of personal health parameters monitor (e.g. heart rate,

blood pressure, oxygen saturation concentration, ...) which

have been produced and used widely. However, most of

users do not have much knowledge of medicine. Perhaps

they can know this parameters, but they cannot know their

health status definitely. And in the other hand, for example,

people with heart disease, although patients can understand

the state of their heart rate, but when one has a heart attack,

without person next to do some helps, he will be in a very

dangerous situation. So a problem arises that how we canmonitor patients health status remotely to be able to solve

the situation. This paper will research to build that system.

An important problem in the process of the system above

is the methods to determine the parameters of personal

health. The classical methods are often complicated, require

the assistance of doctors so it is difficult to use them

individually. For example, the method of determining blood

glucose. Normally to measure this parameter, patients must

take blood from the fingertip, this often causes pains and

risks. The classical method for measuring blood pressure

using pressure cuff manually depends on the experience

of doctors of nurses, therefore it is not used widely for

individuals.

Many methods have been proposed to solve these prob-

lems, such as Photoplethysmography, Electrocardiography,

Electromyography, .... In which, the photoplethysmography

- PPG method has been widely used. PPG [1] is an optical,

non-invasive measuring technique that provides immediate

diagnostic information on the cardiovascular state. PPG

signals reflect the change in blood volume caused by blood

vessel expansion and contraction, which can be detected by

a photodiode if external light is illuminated into the tissue.

Time intervals between the PPG peaks can be converted into

the heart rate or pulse, but the waveform characteristics of

each individual PPG pulse contain hemodynamic informa-tion. The PPG signal consists of two components - a slow,

varying DC offset representing the skin: blood volume in the

probe-covered area, and a fast, alternating AC component

that reflects the blood volume pulsations. The amplitude of

the AC-component is only 0,5-4% of the DC offset in a PPG

signal.

The advantage of the method measuring health parame-

ters based on PPG signal which is simple in theory, low

implementation costs, can be done automatically and easy

deployed in practice widely. In this paper, the method of

determining the value such as: heart rate, blood pressure,

blood glucose-based PPG signal will be introduced. Based

on theoretical study, some methods will be implemented

in MATLAB simulation and on the hardware. The PPG

signals used for determining blood pressure in this paper

are measured on mice and provided by the laboratoryMABEL (Mannheim Biomedical Engineering Laboratories),

Mannheim University [2]. In addition, this laboratory also

provides us with SAM4E-EK board for the construction of

the system. This is a powerful board from Atmel. We will

introduce more about this board lately.

Method of determining the value of heart rate using PPG

signal is implemented on real hardware, the results are

checked with the actual device OMRON. For the method

of determining blood glucose based on PPG signals, this

method is relatively new and is still continuing research. At

present there are only a few actual models of this method,

but they all require complicated techniques. Therefore in this

paper, the method of determining the blood glucose just stop

at theoretical research.

One of the important aspects of research methods is

the PPG signal processing. The majority of signal analysis

methods currently use Fourier analysis. But in practice,

sometimes Fourier analysis has limitations, especially for

unstable signals (non-stationary) as PPG signal. Therefore

this paper is focused on the application of Wavelet transform

in signal processing. Continuous Wavelet Transform (CWT)

and Discrete Wavelet transform (DWT) will be studied to

determine the value of heart rate as well as remove noise

from PPG signals. The rest of this paper focuses on building

a web-based model to monitor personal health parametersbased PPG signal remotely . This process includes: designing

a simple device to measure health parameters; applying

wavelet-based denoising theory in the equipment; building

a Java-based software to display health information as well

as transmit data over the Internet; build a website to monitor

the health parameters remotely.

II. A NON -I NVASIVEB LOOD P RESSUREM EASUREMENT

METHOD U SING P PG S IGNAL

A. Principles

A heterogeneous selection of mice (different age, weightand genealogy) was taken into account in order to validate

the new method without requiring drugs for a specific

increase or reduction of BP. Figure 1 illustrates a schematic

diagram of the device.


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Fig. 1. Schematic diagram for indirect measurement of mouse bloodpressure.

The system consists of two photodiodes a separated a

minimum 15 mm from each other. This distance is based on

the minimum detectable PTT of our system (3 ms) and the

maximal pulse wave velocity reported in mice (4.60 m/s) [3].

They record the reflected light of an infrared LED attachedto the tail of the mice. At the same time, a pressure cuff is

initially inflated to a pressure (180 mm Hg) that exceeds the

systolic arterial pressure of the mice. It is then reduced by

a rate of approximately 2.5 mm Hg/s. The recorded signals

of the photodiodes are then used to estimate the PTT curve,

this curve will be later correlated with the recorded signal

of the cuff pressure. The method described in this paper can

also be implemented in a transmission mode if the distance

between the light source and the sensors is smaller than 1

cm.

The result of the external pressure applied to the artery by

the cuff is the modulation of the artery characteristics suchas the compliance. This modulation can be described through

the formula equation (1) [4].

C(Pi, Pcuff) =R2max

P1(

1

1 + (Pi Pcuff

P1)2

) (1)

P1: relates to the slope at the inflection point, Pi: internal

pressure,Pcuff: cuff pressure,Rmax: is the maximal asymp-

totic vessel radius.

Based on the Bramwell-Hill equation (2) can be demon-

strated the relation between the PTT, the compliance, and

cuff pressure [5].

v= d

PTT =

1

C(Pi, Pcuff) (2)

: blood density, d: distance between sensors.

Figure 2 shows the relation between the PTT and compli-

ance. The three BP values are indicated.

The method used in this study takes advantage of this

effect in order to estimate the BP indirectly. The PTT is

calculated between the recorded signals of the photodiodes.

It is obtained from the sensor nearest to the heart (proximal)

and the signal of the sensor situated further away from the

heart (distal). It shows the three characteristic features forthe determination of the systolic, the mean and the diastolic

BP (figure 3).

The following paragraph explains the method developed

on which we based this study.

Fig. 2. Modulation of the artery characteristics: compliance and PTT asfunction of the internal and the cuff pressure. It shows too the three BPvalues.

Fig. 3. Characteristic points in the PTT curve. The descending curve showsthe cuff pressure.

The cuff is initially inflated to a pressure of 180 mmHg

that exceeds the systolic arterial pressure of the mouse.

As a result of the external pressure, the artery is totallycompressed and therefore the blood cannot circulate and

thus the pulse cannot be detected. The cuff pressure is

reduced by a rate of approximately 2.5 mm Hg/s.

At the time at which the cuff pressure is the same as

the systolic BP, the blood can circulate again and the

wave appears distal of the cuff, the transit time is at its

minimum point. The increase of the transit time curve

is considered a characteristic feature.

Maximum of the transit time curve. At this point,

the cuff pressure is equal to the mean BP. Due to

this pressure; the compliance of the artery is maximal.

Hence, the pulse wave velocity is minimal while the

PTT is maximal [6].

Once the compliance reaches the maximal point it starts

to decrease and with it the PTT. When the cuff pressure

is the same as the diastolic value, the modulation of

the artery be the cuff is completed and the compliance

remains constant. The buckling in the PTT curve is the

diastolic BP.

B. Results

Figure 4 shows a typical change of the transit time

compared to a simultaneous direct BP measurement during

the deflation phase of the cuff. The calculation of the indirect

BP is based on the beat to beat measurement of the transittime and the locations of the three characteristic features:

systolic, diastolic and mean blood pressures.

Figure 5 shows an example of the recordings of the prox-

imal and distal signals (A), the PTT curve as a result of the


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Fig. 4. Typical PTT alterations during a deflation phase of the cuff (X-

axis) compared to the direct measurements (right Y-axis). The vertical linesshow the BP determined by the interpolation with the cuff pressure. At the100 mm Hg cuff pressure the blood could circulate and the transit time isat its minimum point, it is the systolic pressure. At the 80 mm Hg cuffpressure, the pressure is equal to the mean pressure and the PTT curve hasa maximum. At the 60 mm Hg cuff pressure the PTT curve has a bucklingand it is the diastolic pressure.

time difference between both signals with the corresponding

BPs (B) and the cuff pressure (C).

Fig. 5. An example of the recorded distal and proximal signals (A). PTTcurve with the SBP, MBP and DBP points (B). Cuff pressure curve withthe corresponding BP points (C).

Value differences can be explained by the fact that there

is a certain significant distance between the measuring posi-

tioning of the sensors and the heart. Therefore, the indirect

systolic BP values are higher and the mean and diastolic

BP values are lower than those of the direct measurements.

In order to be able to compare our measuring values to the

values described in the literature, the average BP values from

several measuring cycles of a session were calculated. Withthis procedure, the comparison of the blood pressure-value

pairs is more related to the mice than to the measuring cycles.

Table I summarizes the comparison between the direct and

the indirect measurements.

TABLE ISTATISTICALC OMPARISON B ETWEEN D IRECT ANDI NDIRECT B P

MEASUREMENTS

No.DBP MBP SBP

PTT Direct PTT Direct PTT Direct

1 123.7 121 122.5 118 118.5 111

2 116.8 114 113.1 110 106.6 101

3 131.4 133 130.2 128 122.5 120

4 118.5 120 107.8 115 98.06 97

5 116.5 118 109.5 112 96.44 98

6 118.3 121 115.2 112 106.8 105

7 93.8 99 85.45 82 70.2 72

8 113.7 111 96.52 104 88.3 93

9 127.4 129 107.8 118 97.07 103

10 127.8 127 120.8 119 98.06 107

III. A NON -I NVASIVE B LOOD G LUCOSE M EASUREMENT

METHOD U SING P PG S IGNAL

A. Theoretical Analysis

1) Molecular Composition and Relative Absorptivities:

When a photon is incident on a molecule, there will be

bond deformations or bond vibrations at different energy

levels related to different bonds, depending on the energy

of incident photon [7]. So, only the photon with energy that

corresponds to the difference between two of its energy levels

can be absorbed. The frequency of the vibration is given by

the equation (3).

= 1

2

k

m (3)

wherek is the bond strength and m is the reduced mass.For a glucose molecule, the molecular structure is as

shown in figure 6.

Fig. 6. Molecular structure of D-glucose.

Table II shows the frequencies corresponding to different

bond vibrations in glucose molecule [8].

TABLE IIABSORPTIONBANDS OFG LUCOSE B ONDS

Overto ne Wavelength Bond Wavelen gth Bond

Fundamental 3377nm vC-H 2817nm vO-H

First overtone 1688nm 2vC-H 1408nm 2vO-H

Second overtone 1126nm 3vC-H 939nm 3vO-H

At a deeper level absorption of light can be seen as

dependent on the probability of absorbance of a photon by

the molecule. For nth overtone final energy is (n+1)E, whereE is the fundamental energy. As n increases, probability

of absorbance decrease rapidly and hence intensities of

absorbance decrease as overtones increase. The absorption

at fundamental frequency is calculated and from that the


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absorption at second overtone is calculated relatively [9].

Table III depicts the relative absorptivities of C-H bond at

different overtones with respect to the fundamental. It is

evident that the absorptivity decreases rapidly as one goes

from fundamental to the overtones in sequence.

TABLE IIIRELATIVEA BSORPTIVITIES OFC-H BON D

Overtone Wave number cm1 Relative Absorptivity

Fundamental 3019 1

First overtone 5912 0.088

Second overtone 8677 3.2 103

2) Selection of the Wavelength of Peak absorption: The

absorption spectrum of the glucose has been studied in order

to choose the wavelengths for LEDs. For this purpose a IR

absorption spectrum of 0.1M aqueous glucose solution has

been collected and analyzed in second overtone region of

the near-infra red spectra using the Bruker tensor 27 FTIR

spectrometer. Figure 7 shows the absorption spectrum of theglucose over the second overtone region where the selected

wavelengths are shown.

Fig. 7. Spectrum showing the transmittance of glucose.

We can observe that the absorption peaks in this region

are very narrow typically of the order of the 2nm to 5nm but

the LED emits the light over a range of wavelengths. The

wavelengths are chosen such that the weighted average of the

absorption over the spectral bandwidth of the LED is high.

While calculating this weighted average the intensity of light

emitted by the LED acts as weight for the absorption at that

particular wavelength. The expected value of the absorbance

over a range has been calculated as equation (4).

E[A] =

A() f() d (4)

where A is the absorbance of glucose over a range of

wavelengths and f() is the probability density function ofintensity of light emitted.

By multiplying the relative intensity of the light emitted

by the LED shown in figure 8 and the absorbance value

of glucose taken from the spectrum and averaging it over

spectral distribution of LED we calculate the mean value ofthe absorbance which is shown in figure 9.

In order to improve the accuracy of the prediction of blood

glucose level, the wavelengths have been chosen such that

mean value of the absorbance of glucose over the frequency

Fig. 8. Graph showing the relative sensitivity of light emitted by 1070nmLED.

Fig. 9. Intensity weighted mean absorbances over 920nm to 1100nm.

range of LED is high. For the current research, wavelengths

of 935nm, 950nm, and 1070nm have been chosen.

B. Method

According to Beer-lamberts law the absorbance of light

by a liquid is related to the concentration of the material by

equation (5).

A= Cl (5)

where the molar absorptivity of solute at a particular

wavelength, Cis is the concentration of the solute andl is the

path length. Hence for a specific wavelength i, equation (5)

may be written as

Ai = iCili (6)

In our particular case, i= 1 corresponds to a wavelengthof 935nm, i = 2 corresponds to a wavelength of 950nmand i = 3 corresponds to a wavelength of 1070nm. As the

concentrations and path lengths are same for a person at aparticular time, we can write

C1 = C2 = C3 (7)

l1 = l2 = l3 (8)

As discussed in the previous section, three different wave-

lengths have been selected which have peak absorption of

glucose in the near infrared region of the spectrum, 1070, 950

and 935nm. For getting glucose level in blood the absorbance

which is mainly due to blood glucose is calculated from AC

component of PPG wave as follows [10]

OD = log[1 + ItiIti+1

] (9)

where OD is the difference between optical densities attime ti and ti+1, Iti is the pulsatile component at timeti and Iti+1 is the intensity of light at time ti+1.


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This difference has the effect of removing the venous

and the tissue contributions to yield only the change in

intensity due to the pulsating arterial blood compartment

[11], [12]. The optical densities have been calculated at three

wavelengths for 35 subjects. The actual glucose values have

also been determined using standard invasive glucometer.

Regression analysis has been done on these values using

neural network toolbox in MATLAB.

C. Regression Analysis Using Artificial Neutral Networks

Multivariate calibration models have come into wide use

in quantitative spectroscopic analyses due to their ability to

overcome deviations from the Beer Lamberts law caused by

effects such as overlapping spectral bands and interactions

between components [13]. PLS and PCR are the most widely

used chemo-metric techniques for quantitative analysis of

complex multicomponent mixtures. These methods are not

optimal when the relationship between the IR absorbances

and the constituent concentration deviates from linearity.

The theory and the application of Artificial Neural networks

(ANN) in modeling chemical data have been widely pre-sented in the literature [14]. In this current work, ANN has

been used for function fitting to develop a model based on the

PPG data and invasive glucose measurements. For calculating

glucose concentration from beer-lamberts law we can say

that

A[{Ci}, ] =i

(CiAi()) (10)

where Ai() is the optical density at wavelength , ofthe ith component, whose concentration is Ci, A() theoptical density of the mixture. If we have the spectra of all

the components{Ai()} and measure the spectrum of themixture {Ai()}, we can use a mathematical tool to estimatethe{Ci}. The three wavelengths have been chosen suchthat glucose has considerable amount of absorption when

compared to other components. So we have given the matrixA1i=1...25A2i=1...25A3i=1...25

(11)

as input andCi=1...25

as output which are measured using

standard invasive method. In this way we have three different

optical densities as inputs and one output for a single sample.

MATLAB neural network toolbox has been used. A two

layer feed forward network with sigmoid hidden neurons and

linear output neurons has been chosen. Number of hidden

layers have been chosen as 10.

IV. APPLICATION OF WAVELETS IN P PG S IGNAL

PROCESSING

A. PPG Signal Improvement Using DWT

The PPG signal profile has a very small amplitude, so

some artifact can arise due to patient motion during measure-

ment that are of comparable amplitude, as well as transport

artifacts arising when a patient is being transported by

ambulance. The other artifact can be patients body vibration,

which can have large amplitude and a frequency close to the

patient heart rate. This close proximity in frequency makesremoval of vibration artifact from PPG signals especially

difficult by Fourier techniques. The PPG signal has been

used for recognizing much variabilitys of health, so it is very

important to get the parameters of this signal clear without

noises and artifacts in order to support clinical decision

making. To address this issue, DWT allows effective noise

reduction. The DWT splits the signal into two components,

each of half the original length, with one containing the

low-frequency or smooth information and the other the

high-frequency or difference information. The process is

performed again on the smooth component, breaking it

up into low-low and high-low components and it is

repeated several times. The simpler way to remove noise orto reconstruct the original signal from a contaminated signal,

in case of 1D or 2D, using the wavelet coefficients which

are the result of decomposition in wavelet transform, is to

eliminate the small coefficient associated to the noise. After

updating the coefficients by removing the small coefficients

assuming as noise, the original signal can be obtained by

the reconstruction algorithm using the noise free coefficients.

Because it is usually considered that the noise has high

frequency coefficients, the elimination of the small coeffi-

cient generally applied on the detail coefficients after the

decomposition. Indeed, the main idea of the wavelets de-

noising method to obtain the ideal components of the signalfrom the noisy signal requires the estimation of the noise

level. The estimated noise level is used in order to threshold

the small coefficient assumed as noise. The noise reduction

procedure follows the flowchart in figure 10.

Fig. 10. The flowchart of the noise reduction algorithm.

The crucial issue of this approach is determination of an

appropriate threshold value. In this paper, the authors applies

the following hard thresholding rule on the coefficients ci,k.

ci,k =

ci,k, |ci,k| i0, |ci,k| i

(12)

wherei is the threshold that depends on the noise level at

the ith level; the signal is then reconstructed by the IDWT

of the ci,k coefficients. It is essential to find the appropriatevalue for the threshold. In this paper, we use the universal

threshold estimator proposed by Donoho. It uses a fixed

threshold form given as:

=

2log n (13)

where n denotes the length of the analyzed signal and is

given by:

=median(dL,k)

0.6745 (14)

wheredL,k are the coefficients at the highest decomposition

level.

In this section, the authors implement the noise reduction

algorithm in MATLAB. Firstly the considered signal was

decomposed using a five-level wavelets decomposition. Thereason for five-level decomposition is that it is the maximum

level that the approximation component is least distorted

compared to the original signal. The other factor deciding

this methods efficiency is the mother wavelet. One of the


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key criteria of a good mother wavelet is its ability to fully

reconstruct the signal from the wavelets decompositions. We

used in our analysis the Daubechies db4. Figure 11 shows

DWT results with all approximation and detail components.

Fig. 11. Signal decomposition using db4 mother wavelet.

The high frequency components of the signal decreases

as lower details are removed from the original signal. As

the lower details are removed, the signal becomes smoother

and the noises disappears since noises are marked by high

frequency components picked up along the ways of trans-

mission. This is the contribution of the discrete wavelet

transform where noise filtration is performed implicitly.

Figure 12 presents the signal before and after denoising.

Fig. 12. Removing background noise from the PPG signal.

In addition, the PPG signal artifact removal algorithm

also makes use of DWT. This algorithm is based on the

observation that the discrete wavelet transform puts thebiomedical waveform in a very different region of the

transform plane than the signal components attributable to

artifact. This can be seen clearly by a visualization of the

discrete wavelet transform in which the absolute values of

the wavelet transform coefficients, as a function of time

and level of the transform (scale), usually called time-scale

plot, or scalogram. The artifacts, if show up, usually present

most prominent in the short scale (high level of transform)

portions of the scalogram. So in order to remove the artifacts,

these portions should be zeroed out. The rest of the DWT

coefficients may then be reconstructed to be used as an input

to an PPG pressure determination algorithm in the usual way

for the measurement of desired patient pressure values.

B. PPG Signal Frequency Estimation Using CWT

In order to estimate PPG signal frequency using CWT,

there is two problems that we have to clarify, they are

scale and frequency. Obviously, there is clearly a relationship

between scale and frequency. Recall that higher scales corre-

spond to the most stretched wavelets. The more stretched

the wavelets, the longer the portion of the signal with which

it is being compared, and therefore the coarser the signal

features measured by the wavelet coefficients. To summarize,

the general correspondence between scale and frequency is:

Low scale a Compressed wavelets Rapidly chang-ing details High frequency . High scale a Stretched wavelets Slowly changing,

coarse features Low frequency .While there is a general relationship between scale and

frequency, no precise relationship exists. Users familiar with

Fourier analysis often want to define a mapping between a

wavelet at a given scale with a specified sampling period

to a frequency in hertz. You can only do this in a general

sense. Therefore, it is better to talk about the pseudo fre-

quency corresponding to a scale. The Wavelet ToolboxTM

in MATLAB provides two functions centfrq and scal2frq,

which enable you to find these approximate scale-frequency

relationships for specified wavelets and scales. The algorithm

of this method follows the flow chart below (figure 13).

Fig. 13. The flowchart of the frequency estimation algorithm.

The important factors influencing algorithms result are

scale and Wavelet family. In our calculation, signals fre-

quency is usually between 5 and 15 Hz, so we choose

scale with the minimum value is 50, the maximum value

is 256, and the step is 0.1. The other important condi-

tion affecting the result of wavelet transform is the base

(mother) wavelet.The advantages of wavelet transform for

signal analysis is the abundance of the base wavelets. From

such abundance arises a natural question of how to choose a

base wavelet that is best suited for analyzing a specific signal.

Since the choice in the first place may affect the result of

wavelet transform at the end, the question is valid.

The complex-valued Morlets wavelet is often selected

as the choice for signal analysis using the CWT. Morlets

wavelet insures that the time-scale representation can be

viewed as a time-frequency distribution. This wavelet has

the best representation in both time and frequency because

it is based on the Gaussian window. The Gaussian function

guarantees a minimum time-bandwidth product, providing

for maximum concentration in both time and frequency

domains. This is the best compromise for a simultaneous

localization in both time and frequency as the Gaussian

functions Fourier transform is simply a scaled version of

its time domain function. In order to evaluate algorithms

quality, first we implement 16384-point FFT (best fit signalslength). Figure 14 shows spectrum plot.

We can see the fundamental frequency of this signal is

9.56 Hz. To evaluate this method, we apply the algorithm

with 4 different wavelet functions: Haar, Morlet, Daubechies


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Fig. 14. Spectrum plot of PPG signal.

4 and Coiflet 1. Figure 15 is the CWT result with the Morlet

mother wavelet.

Fig. 15. Continuous Wavelet Transform visualization.

The area with lighter color represents large coefficients

area. From the flowchart and algorithm, max coefficientcorresponds to scale value 88.2, and so we get frequency

9.43 Hz. We can see that with the Morlet mother wavelet,

the Wavelets based approach gives a similar result to the

Fourier based one. Table IV shows the results of the other

mother Wavelet as well as the other signals.

TABLE IVRESULT OF A PPLYINGA LGORITHM TOS OM E P PG S IGNALS

No. FFT Morlet Haar Db4 Coif1

1 10.56 10.43 11.93 9.64 11.17

2 9.56 9.40 10.21 8.60 9.90

3 11.25 11.39 11.93 9.72 11.22

4 9.68 9.78 20.4 7.45 8.20

5 8.75 8.72 9.94 8.70 9.60

Based on the tables, different wavelet functions produces

different results compared to the Fourier transform. The

Morlet function has the smallest error (less than 2%), these

results can be accepted, but the Haar function has the largest

error value, so it isnt suitable for signal processing. Other

functions, such as db4 and Coif 1, have the error less than

the Haar function but it is still not acceptable.

V. SIGNAL D ENOISINGU SINGDWT ON H ARDWARE

DWT with db4 mother Wavelet has proved its effective-

ness. Noise is basicly removed from the original signal.

In this section, we will introduce a method to implement

DWT with db4 on hardware. The coefficients of db4s scale

function are:

h0 = 1 +

3

4

2; h1 =

3 +

3

4

2; h2 =

3 34

2; h3 =

1 34

2(15)

The wavelet function coefficients are:

g0 = h3; g1 =

h2; g2 = h1; g3=

h0 (16)

Each step of the wavelet transform applies the scale

function and wavelet function to the input data. If the original

data set has N values, the wavelet function will be applied to

calculate N/2 smoothed values and N/2 differences (reflecting

change in the data). In the forward wavelet transform, the

smoothed values are stored in the lower half of the N element

input vector and the differences are stored in the upper half

of the N element input vector.

The scaling and wavelet functions are calculated by taking

the inner product of the coefficients and four data values. The

equations are shown in equation (17) and equation (18).

Scaling function:

ai = h0s2i+h1s2i+1+h2s2i+2+h3s2i+3 (17)

Wavelets function:

ci = g0s2i+g1s2i+1+g2s2i+2+g3s2i+3 (18)

Each iteration in the wavelet transform step calculates a

scaling function value and a wavelet function value. The

index i is incremented by two with each iteration, and new

scaling and wavelet function values are calculated. In the case

of the forward transform, with a finite data set (as opposed

to the mathematicians imaginary infinite data set), i will be

incremented until it is equal to N-2. In the last iteration the

inner product will be calculated from calculated from s[N-2], s[N-1], s[N] and s[N+1]. Since s[N] and s[N+1] dont

exist (they are beyond the end of the array), this presents

a problem. This is shown in the transform matrix below

(figure 16). Note that this problem does not exist for the

Fig. 16. Forward transform matrix for an 8 element signal.

Haar wavelet, since it is calculated on only two elements,

s[i] and s[i+1]. A similar problem exists in the case of the

inverse transform. Here the inverse transform coefficients

extend beyond the beginning of the data, where the first

two inverse values are calculated from s[-2], s[-1], s[0] and

s[1]. This is shown in the inverse transform matrix below

(figure 17).

Three methods for handling the edge problem:

Treat the data set as if it is periodic. The beginning ofthe data sequence repeats rolling the end of the sequence

(in the case of the forward transform) and the end of

the data wraps around to the beginning (in the case of

the inverse transform).


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Fig. 17. Inverse transform matrix for an 8 element signal.

Treat the data set as if it is mirrored at the ends. This

means that the data is reflected from each end, as if a

mirror were held up to each end of the data sequence.

Gram-Schmidt orthogonalization. Gram-Schmidt or-

thogonalization calculates special scaling and wavelet

functions that are applied at the start and end of the

data set.

Zeros can also be used to fill in for the missing elements,

but this can introduce significant error. The algorithm pub-lished in this paper treats the data as if it were periodic.

After building DWT and IDWT algorithm, the next thing

that we have to do is to build the de-noising algorithm on

hardware. First of all, we need to estimate the threshold used

for de-noising. Using Donohos method as discussed in the

previous section, now we need to build a function to calculate

median. According to the median algorithm, firstly input data

sequence x with data length N are arranged from small to

large. Then:

median(x) =

x[n

2] +x[

n

2 1]

2 if N is evenx[

n

2] if N is odd

(19)

After calculating the median, using equation (13) and

equation (14) to calculate the threshold value. After deter-

mining the threshold, the samples of signals whose absolute

values are less than the threshold are set to 0 (hard threshold

method). The remaining values will be preserved.

V I. SYSTEMD ESIGN

A. Hardware Implementation

To calculate the heart rate continuously, a sensor is de-

signed to acquire the PPG signal. The proposed design of

the hardware is shown in figure 18.

Fig. 18. Proposed hardware design.

The transmitter is a LED emitting light in the near IR

whose wavelength is 940 nm. The LEDs intensity is driven

by a Digital-to-Analog module (DAC) of the microcontroller(MCU). After the transmitted infrared signal passes through

the test site (usually a reasonably translucent area with good

blood flow, such as a finger or earlobe), it is received at

the photo-detector. However, this is a current signal and its

amplitude is very small. Thus it needs to be amplified and

converted to a voltage signal so that it can be measured by

MCUs Analog-to-Digital module (ADC). This amplifying

and converting module is called transimpedance (TIA). In

practice, we can use the IC OPT101 by Texas Instruments.

This IC already contains a TIA module and a photodiode to

detect light signal.

The signal after photo-detector contains lots of other

undesired frequency components. Thus, a band-pass filter isneeded to remove the undesired signal, leaving the frequency

which is desired. Peoples heart rate is from around 40 - 160.

The filters pass band should be in this range. The receiver

filter is set 0.8 Hz to 3 Hz as the pass band. In addition, the

filter should provide some gain added to the signal, because

even though TIA is put behind photo-detector, the overall

gain is not enough. Figure 19 shows a proposed design

of the filter and amplifier stage. Using Multisim Electronic

Workbench to simulate this design, the actual pass band is

0.714 - 3.2 Hz, the total gain is about 38 dB (it means the

PPG signal will be amplified up to 80 times).

Fig. 19. Schematic of the filter and amplifier stage.

The most important module of this design is the MCU.

The more powerful the MCU is, the higher efficiency we

will get. Nowadays, there are plenty of MCUs from many

manufacturers, such as TI, Atmel, Microchip, STMicroelec-

tronics In this paper, An ARM Cortex-M4 processor-based

MCU is used. Its called SAM4E16E. For convenience, this

MCU is integrated to an evaluation board called SAM4E-

EK, which is shown in figure 20. The language used for

programming is C/C++. We use Iar Embedded Workbench

as an IDE to compile code and download to board.

Fig. 20. SAM4E evaluation board.

MCU will process the PPG signal to calculate the heart

beat value. This heart beat value will be sent to PC via UART

and displayed in a Graphic User Interface (GUI).


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B. Software Implementation

1) Board Programming: As discussed in the theory sec-

tion, the signal received from the photo-detector and the

human heart beat is congruent. It means every time your

heart beats, it generates a pulse signal. Although the signal

received from the photo-detector is weak, but after the block

of filtering and amplification, the signal is amplified many

times and can be measured by the ADC on board SAM4E-EK. Heart rate is the number of beats in 60 seconds, but

if you read all data in the 60s, it will take a lot of time. A

more simple way, because heart rate usually does not change

continuously, so we can just read the ADC data in 10s, then

use FFT to calculate signals frequency multiply with 60,

we will get your heart rate parameters. An alternative way

to calculate heart beat is using the Timer Counter function

of MCU. Signal passing through the filter and amplifier will

become a pulse, so it can be counted by Timer Counter. This

result is then multiply with 6 only. Both FFT and Timer

Counter methods have their own advantages and disadvan-

tages. FFT method requires a large memory, because it needs

at least 8192 points to give an acceptable resolution (the

suggestion is 16384 points). However, the higher resolution

is, the higher accurate the result is. With Timer Counter

method, memory requirement is not a problem, but the result

is less accurate than FFT. Figure 21 shows the flowchart of

the main program.

Fig. 21. Main program flowchart.

While doing delay routine, the Interrupt Service Routine

(ISR) will execute. In the ISR, ADC module will read the

converted result and save to the memory; or the Timer

Counter will read the number of pulse appearing if we use

the second method. Figure 22 shows the flowchart of the

ISR. A real device has been used to check the accuracy of

this design. This device is manufactured by OMRON. Some

tests were made on some people using both this design and

OMRON device. Table V shows the results of these tests.

As we can see, the difference between them is not really

significant. Instead of buying an expensive device, we can

use this design to get an acceptable result.2) Designing PC Program: As discussed above, the heart

rate value after being calculated will be sent to PC and

displayed in a GUI. The GUI can be created by various of

language such as Java, C/C++, C#... In this paper, we use

Fig. 22. ISR program flowchart.

TABLE VPROPOSEDD ESIGN VSO MROND EVICE

No. Proposed design OMRON device Error (%)

1 81 80 1.25

2 73 73 0.00

3 66 67 1.49

4 50 52 3.85

5 77 77 0.00

6 55 56 1.79

7 61 61 0.00

8 69 69 0.00

9 90 88 2.27

10 70 71 1.41

Java because it is a powerful language, with high perfor-

mance and security. More over, a Java program just needs

to be written once on a platform (like Windows, Unix...),

and when we bring it to another platform, it still works

well. First of all, we need to build a connection between

PC and board. One of the most common interface used onMCU is UART. SAM4E-EK has already integrated a RS232

port. The advantages of this interface are simplicity and

convenience. Therefore in this paper, we use UART as the

interface between PC and board. In Java, the library for this

interface is called Java Communication API (also known as

javax.com). However, this library supports Linux and Solaris

platforms only. To be able to use this interface in Windows,

it is necessary to install a third-party library called RXTX.

This library is available at www.rxtx.qbang.org. Figure 23

is the GUI of the UART setting. On this GUI, the user can

select the COM port to communicate as well as install the

parameters of the UART interface such as Stop bits, FlowControl, Data bits ...

Fig. 23. UART interface setting.

The next thing is the main GUI. In this main GUI, the

health information such as heart rate, blood pressure and


10/10

blood glucose will be displayed. Figure 24 shows the main

GUI that we have designed.

Fig. 24. The main GUI.

In the system that were building, each time a user wants

to update their health information to the server, they need

to provide their personal information. So we need to build

a GUI to enter information. This GUI can be constructed as

shown in figure 25.

Fig. 25. The GUI for entering information.

The final function of the PC program that we need to

build is the connection to Internet, because we need to

upload the information such as health parameters, personalinformation... to the web-server. In Java, the java.net library

provides the methods to access to the Internet. After being

constructed, this program will continuously read data via

UART interface and show up to the GUI. Each time user

fills in their information and presses the button Update,

data will be sent via Internet to the web server.

3) Designing Web Page: There are plenty of language that

we can use to design a web page. In this paper, we choose

ASP.NET to create our web page. The reason for choosing

ASP.NET because it is a powerful platform supported by

Microsoft, with a simple programming model, supporting

multi-language, high security ability, convenient for web

application configuration. Figure 26 is the designed web

page.

Fig. 26. Web page for monitoring health information.

VII. CONCLUSION

This paper has focused on researching methods of de-

termining the health parameters such as the heart rate, the

blood pressure, the blood glucose by non-invasive, using the

algorithms to analyze the PPG signal to determine the desired

parameters. In the other hand, we have also researched the

theory and application of Wavelets in Biomedical Signal

Processing, they are the use of CWT to determine the value

of heart rate and DWT to remove noise from original PPG

signals. The denoising method based on DWT has been also

implemented on hardware. In addition, a personal health

information system has been designed. This system contains

a circuit to measure heart rate value of humans with the

support of SAM4E-EK board, a software to display and

transmit those values over the Internet, a website system

to display measured results for remote monitoring. In the

future, we plan to research some other methods to determine

more health parameters and develop this system to give users

more advantages, such as a mobile-based health monitoring

system.

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Designing a web-based health information system